Add Agentic LangGraph RAG Tutorial with Fixed Image Paths

Agentic-langGraph-RAG/readme.md

@@ -0,0 +1,144 @@
+# Agentic PDF RAG System
+
+An intelligent document analysis system that combines PDF processing, vector search, and agentic AI workflows to provide accurate answers from document content.
+
+## Overview
+
+This system processes PDF documents by converting them to images, extracting text using GPT-4o vision capabilities, and creating a searchable knowledge base. An intelligent agent then handles user queries by deciding when to retrieve information, evaluating document relevance, and generating accurate responses.
+
+## Key Features
+
+- **PDF to Text Conversion**: Uses pypdfium2 to convert PDF pages to high-quality images
+- **OCR with GPT-4o**: Leverages GPT-4o vision model for accurate text extraction
+- **Vector Database**: Stores embeddings in Qdrant for efficient similarity search
+- **Intelligent Agent**: LangGraph-powered agent that makes smart retrieval decisions
+- **Document Grading**: Automatically evaluates relevance of retrieved documents
+- **Query Rewriting**: Improves queries when initial results are not relevant
+- **Expanding Window Training**: Demonstrates advanced ML techniques for stock prediction
+
+## System Architecture
+
+```mermaid
+flowchart TD
+    A[PDF Document] --> B[Convert Pages to Images]
+    B --> C[GPT-4o OCR Text Extraction]
+    C --> D[Text Preprocessing & Chunking]
+    D --> E[Generate Embeddings]
+    E --> F[Store in Qdrant Vector DB]
+
+    G[User Query] --> H[Agent Node]
+    H --> I{Retrieve Documents?}
+
+    I -->|Yes| J[Vector Search & Retrieval]
+    I -->|No| K[Direct Response]
+
+    J --> L[Grade Document Relevance]
+    L --> M{Documents Relevant?}
+
+    M -->|Yes| N[Generate Answer]
+    M -->|No| O[Rewrite Query]
+
+    O --> H
+    N --> P[Final Response]
+    K --> P
+
+    F -.-> J
+
+    subgraph "Document Processing"
+        A
+        B
+        C
+        D
+        E
+        F
+    end
+
+    subgraph "Query Processing"
+        G
+        H
+        I
+        J
+        L
+        M
+        N
+        O
+        K
+        P
+    end
+```
+
+## How It Works
+
+### Document Processing
+1. **PDF Conversion**: PDF pages are converted to high-resolution images
+2. **Text Extraction**: GPT-4o analyzes images and extracts text content
+3. **Preprocessing**: Text is cleaned, chunked, and prepared for embedding
+4. **Vector Storage**: Document chunks are embedded and stored in Qdrant
+
+### Query Processing
+1. **Agent Decision**: Intelligent agent decides whether to retrieve documents
+2. **Vector Search**: If needed, performs similarity search in vector database
+3. **Relevance Grading**: Evaluates if retrieved documents answer the query
+4. **Response Generation**: Creates final answer or rewrites query if needed
+
+## Example Use Case
+
+The system is demonstrated with a research paper on "Stock Price Prediction Using Hybrid LSTM-GNN Model". Users can ask questions like:
+
+- "How is the graph constructed for the GNN component?"
+- "What is the MSE of CNN in Figure 5?"
+- "What are the test days with highest MSE values?"
+
+## Technologies Used
+
+- **LangChain**: Framework for building LLM applications
+- **LangGraph**: Agent workflow orchestration
+- **OpenAI GPT-4o**: Vision and text generation model
+- **Qdrant**: Vector database for similarity search
+- **pypdfium2**: PDF processing and image conversion
+- **Python**: Core programming language
+
+## Installation
+
+```bash
+pip install pypdfium2 backoff langchain-community langchain langchain-openai langgraph qdrant-client
+```
+
+## Configuration
+
+Set your API keys:
+```python
+OPENAI_API_KEY = "your-openai-api-key"
+QDRANT_API_KEY = "your-qdrant-api-key"  # Optional for local deployment
+QDRANT_URL = "your-qdrant-url"          # Optional for local deployment
+```
+
+## Usage
+
+1. Load your PDF document
+2. Run the document processing pipeline
+3. Start querying the system with natural language questions
+4. The agent will intelligently retrieve and process relevant information
+
+## Benefits
+
+- **Intelligent Retrieval**: Only searches when necessary
+- **Quality Control**: Validates document relevance before responding
+- **Adaptive**: Improves queries automatically when initial results are poor
+- **Accurate**: Combines vision-based OCR with semantic search
+- **Scalable**: Vector database enables fast search across large document collections
+
+This system demonstrates advanced RAG (Retrieval-Augmented Generation) techniques with agentic AI workflows for robust document analysis and question answering.
+
+## Tutorial Article 
+For a detailed step-by-step guide on building this system, read the full tutorial on Medium:
+[**How I Built an Agentic RAG System with Qdrant to Chat with Any PDF**](https://medium.com/@mohammedarbinsibi/how-i-built-an-agentic-rag-system-with-qdrant-to-chat-with-any-pdf-4f680e93397e)
+
+
+
+### References : 
+* [LangChain](https://github.com/langchain-ai/langchain)
+* [LangGraph](https://langchain-ai.github.io/langgraph/)
+* [LangGraph Agentic RAG](https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_agentic_rag.ipynb)
+* [Qdrant documentation](https://qdrant.tech/documentation/)
+* [LSTM-GNN paper](https://arxiv.org/pdf/2502.15813)

README.md

@@ -15,4 +15,5 @@ This repo contains a collection of tutorials, demos, and how-to guides on how to
 | [Step-back prompting in Langchain RAG](./langchain-qdrant-step-back-prompting)            | Step-back prompting for RAG, implemented in Langchain                                      | OpenAI, Qdrant, Cohere, Langchain                                            |
 | [Collaborative Filtering and MovieLens](./sparse-vectors-movies-reco)                     | A notebook demonstrating how to build a collaborative filtering system using Qdrant        | Sparse Vectors, Qdrant                                                       |
 | [Use semantic search to navigate your codebase](./code-search/)                           | Implement semantic search application for code search task                                 | Qdrant, Python, sentence-transformers, Jina                                  |
+| [Agentic-langGraph-RAG Tutorial](./Agentic-langGraph-RAG/Agentic_PDF_RAG.ipynb)           | Tutorial for Agentic RAG using LangGraph and Qdrant                                        | LangGraph, Qdrant, GPT-4o, RAG                                                   |
 

qdrant_101_audio_data/README.md

@@ -1,6 +1,6 @@
 # Qdrant & Audio Data
 
-![main](../images/main_pic.png)
+![main](./img/main_pic.png)
 
 Welcome to this tutorial on vector databases and music recommendation systems using Python and Qdrant. Here, 
 we will learn about how to get started with audio data, embeddings and vector databases.

qdrant_101_text_data/README.md

@@ -1,6 +1,6 @@
 # Qdrant & Text Data
 
-![qdrant](../images/crab_nlp.png)
+![qdrant](./img/crab_nlp.png)
 
 This tutorial will show you how to use Qdrant to develop a semantic search service. At its core, this service will harness Natural Language Processing (NLP) methods and use Qdrant's API to store, search, and manage vectors with an additional payload.